Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

Abstract

38Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients 39 ultimately relapse and many become refractory to their previous therapies. Although many 40 therapies exist with diverse mechanisms of action, it is not yet clear how the differences in MM 41 biology across patients impacts the likelihood of success for existing therapies and those in the 42 pipeline. Therefore, we not only need the ability to predict which patients are at high risk for 43 disease progression, but also a means to understand the mechanisms underlying their risk. We 44 hypothesized that knowledge of the biological networks that give rise to MM, specifically the 45 transcriptional regulatory network (TRN) and the mechanisms by which mutations impact gene 46 regulation, would enable improved predictions of disease progression and actionable insights for 47 treatment. Here we present a method to infer TRNs from multi-omics data and apply it to the 48 generation of a MM TRN that links chromosomal abnormalities and somatic mutations to 49 downstream effects on gene expression via perturbation of transcriptional regulators. We find that 50 141 genetic programs underlie the disease and that the activity profile of these programs fall into 51 one of 25 distinct transcriptional states. These transcriptional signatures prove to be more 52 predictive of outcomes than do mutations and reveal plausible mechanisms for relapse, including 53 the establishment of an immuno-suppressive microenvironment. Moreover, we observe subtype-54 specific vulnerabilities to interventions with existing drugs and motivate the development of new 55 targeted therapies that appear especially promising for relapsed refractory MM. 56 57 Introduction 58 59 Multiple Myeloma (MM) is a cancer of malignant plasma cells in the bone marrow (BM) that has 60 a prevalence of approximately 86,000 new cases per year. 1 Several clinical subtypes of MM have 61 been established on the basis of characteristic cytogenetic features, including various 62 translocations, gain or loss of chromosomal arms, deletion of specific chromosomes, and 63 hyperdiploidy. 2-4 Accordingly, MM is a complex disease of great heterogeneity that exhibits64 subtype-specific drivers of progression. 5,6 Efforts to better characterize the biology and 65 therapeutic vulnerabilities of MM have increased exponentially in recent years, as can be seen 66 from the number of research articles, clinical trials, and public availability of matched genomic, 67 transcriptomic, and patient data. However, the myriad combinations of chromosomal aberrations 68 and somatic mutations, coupled with the complex dependence of MM progression on the BM 69 microenvironment, have precluded a mechanistic understanding of the disease on a patient-70 specific level. 72The 5-year survival rate of MM is approximately 50% 7 , which is nearly double what it was in the 73 late 1980s. 8 The reason for the improved outcomes is a series of therapeutic advances that 74 include the introduction of autologous stem cell t...

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

“…Regulons are identified, in part, by performing PCA on the normalized scRNA-seq data profiles to identify principal components in which decreasing amounts of variation across genes are captured along each principal component – defined as a linear combination of gene expression values. This linear combination of weighted gene expression values defines the eigengene value per sample ( 41 , 42 , 86 , 87 ). Alternatively, the eigengene is defined as the first principal component of the module expression matrix composed of expression values of regulon genes across samples.…”

“…To infer regulons within single cells, we applied the MINER ( 86 ) workflow to the SN520 and SN503 scRNA-seq data sets independently. As part of the scSYGNAL framework, the MINER algorithm involves a suite of functions that enables the inference of causal mechanistic relationships linking genetic mutations to transcriptional regulation.…”

“…We applied single-cell SYstems Genetics Network AnaLysis (scSYGNAL) framework to uncover the transcriptional regulatory networks (TRNs, (43,44)) responsible for driving the distinct transcriptome responses of the two PD-GSCs. Briefly, Mechanistic Inference of Node Edge Relationships (MINER), an algorithm within the scSYGNAL framework, was used to identify modules of genes (regulons) that were co-regulated differentially in response to treatment across sub-populations of cells (45,46). Further, using the transcription factor binding site database (47) and the Framework for Inference of Regulation by miRNAs (FIRM, (48)), scSYGNAL implicated specific TFs and miRNAs in mechanistically co-regulating genes of all regulons.…”

A single-cell based precision medicine approach using glioblastoma patient-specific models

“…Our analysis revealed the mechanistic co-regulation of 809 genes across 160 regulons by at least 65 TFs and 141 miRNAs (Supplementary Tables 3, 4). Subsequent analysis via Mining for Node Edge Relationships (MINER) algorithm 13 revealed a subset of 93 significant regulons that included 52 nonredundant TFs regulating 454 target genes (Methods, Supplementary Tables 3, 4). Of the 52 TFs identified, 33 TFs (63.4%, p value = 0.009) have been reported to play biologically meaningful roles in GBM 12,14 , indicating that the single-cell-level network analysis had identified GBM-relevant regulatory mechanisms (Supplementary Table 5).…”

“…To infer regulons within single cells, we applied the SYGNAL 12 and MINER 13 workflow to the snRNA-seq dataset resulting from the Batch Integration procedure described above. The MINER algorithm involves a suite of functions that enables the inference of causal mechanistic relationships linking genetic mutations to transcriptional regulation.…”